Papers by Mostafa Mirshekari
ChainCQG: Flow-Aware Conversational Question Generation (2021.eacl-main)
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| Challenge: | Current datasets for conversational question answering lack realistic, domain-specific training data. |
| Approach: | They propose a model that generates question-answer representations across dialogue turns . they use flow propagation training to improve conversational flow and fluidity . |
| Outcome: | The proposed model outperforms answer-aware and answer-unaware SOTA baselines significantly . it generates different types of questions with improved fluidity and coreference alignment. |